Social media have profoundly changed social communication practices across a vast range of contexts. To theorize these changes, numerous authors have proposed digital affordances as a conceptual lens. Yet, to date, most accounts of digital affordances either gloss broadly over crossplatform or use-dependent differences in practices; or they are highly context-specific, obstructing theoretical integration. In this article, we conceptualize social media affordances on an intermediate level of abstraction that foregrounds consequential differences in how digital social media platforms structure social communication practices. Focusing on the characteristic communication needs of social movements as an exemplary case, we identify how social media platforms present users with differential affordances for articulating public claims, building collective identities, and mobilizing contentious performances. We examine how key contextual conditions alter the value of differential affordances, potentially resulting in differential communication practices and platform preferences. We conclude by discussing key opportunities of our approach for comparative research and theory building.

This article examines the journalistic production of mediated political projections – media narratives about uncertain political futures, such as anticipated election outcomes and their implications. Despite the significance of prospective coverage in political journalism and its influence on political decision-making, there is limited understanding of journalists’ perceptions and textual expressions of political forecasting. Drawing on interviews with Israeli journalists and a computational text analysis of election coverage in France, Israel, and the U.S., this study aims to understand how journalists perceive, negotiate, and textually navigate political forecasting in their work – whether through their own projections or by mediating forecasts made by others. The findings reveal journalists’ deep ambivalence toward political forecasting and the resulting textual practices. We show how journalists attribute their engagement in forecasting to external pressures, while their reluctance stems from the inherent risks and challenges associated with political forecasting and its tension with their journalistic identity and professional values. To navigate this tension, they incorporate projections into conventional factual reporting or use non-committal language. Except in data journalism, assessing the likelihood of political scenarios is uncommon. Although these patterns are observed across countries and media types, prospective coverage is more prevalent in interventionist and accommodative journalistic cultures, with the rhetoric of facticity and certitude more common in broadcast news. We suggest that journalists’ reluctance to fully engage with the inherent uncertainties of political futures limits their ability to contribute effectively to public decision-making processes as societies navigate political futures.

This article examines the journalistic production of mediated political projections—media narratives about uncertain political futures, such as anticipated election outcomes and their implications. Despite the significance of prospective coverage in political journalism and its influence on political decision-making, there is limited understanding of journalists’ perceptions and textual expressions of political forecasting. Drawing on interviews with Israeli journalists and a computational text analysis of election coverage in France, Israel, and the U.S., this study aims to understand how journalists perceive, negotiate, and textually navigate political forecasting in their work—whether through their own projections or by mediating forecasts made by others. The findings reveal journalists’ deep ambivalence toward political forecasting and the resulting textual practices. We show how journalists attribute their engagement in forecasting to external pressures, while their reluctance stems from the inherent risks and challenges associated with political forecasting and its tension with their journalistic identity and professional values. To navigate this tension, they incorporate projections into conventional factual reporting or use non-committal language. Except in data journalism, assessing the likelihood of political scenarios is uncommon. Although these patterns are observed across countries and media types, prospective coverage is more prevalent in interventionist and accommodative journalistic cultures, with the rhetoric of facticity and certitude more common in broadcast news. We suggest that journalists’ reluctance to fully engage with the inherent uncertainties of political futures limits their ability to contribute effectively to public decision-making processes as societies navigate political futures.

Following the progressing internationalisation of social science research and the computational turn in the field, researchers are increasingly adopting computational text analysis (CTA) methods to compare textual data across multiple cases and languages. In these settings, it is not only the mapping between construct and measures that requires validation, but also the equivalence of this mapping across languages and cases. However, although the validation requirements in multilingual analyses exceed those in monolingual studies, current research shows that validation is often insufficiently and inconsistently addressed in comparative multilingual CTA. To support more robust comparative research, this article presents a framework for validating findings obtained from multilingual textual data. The framework outlines validation strategies for four key stages of a typical multilingual CTA workflow: corpus, input data, process, and output. It directly tackles the challenge of approaching equivalence across contexts and languages in these stages and moves beyond the common practice of identifying problems only at the final stage of research.

Digital media constitute a key space for the negotiation of public opinion. Despite long-standing research on public opinion climates on digital media, little theory exists that considers their emergence and discursive dynamics. In this article, we conceptualize public opinion as a discursive process, which revolves around the public negotiation of normatively acceptable opinions. Reviewing and updating theoretical work on public opinion negotiations in pre-digital media, we examine how digital media have transformed this discursive process. Focusing on a) the public expression of b) normative opinions upon public issues by c) positioned speakers, resulting in d) the public negotiation of acceptable stances e) within public arenas governed by institutional and socio-technical media logics, we propose a theory of public opinion negotiations in a digital media ecosystem. Based on our conceptualization, we discuss operational implications for the empirical study of public opinion processes on digital media.

Traditional agenda-setting research often focuses on the most urgent problems that dominate present public agendas. Challenging the prevalent conflation of importance with urgency in agenda-setting research, this article proposes a shift from a singular to a layered temporal conceptualization of public agendas. The suggested framework distinguishes between the immediate agenda, which addresses problems perceived as most urgent, and the delayed agenda, which focuses on issues deemed most important for the future. Enriching agenda-setting theory with insights from construal level theory, the study examines the psychological and media-related factors shaping the placement of topics on these agendas. Drawing upon original survey data and a large-scale news content analysis from the French 2022 Presidential Elections, as well as survey data from the 2023 to 2024 war in Israel and Gaza, the findings provide empirical support for the proposed framework. The results indicate that participants prioritize psychologically proximate issues on the immediate agenda, whereas psychologically distant issues gain importance on the delayed agenda. Additionally, we identify media agenda-setting effects that extend beyond the immediate temporal layer. Specifically, both studies provide evidence for a media priming effect, where news exposure increases issue salience without affecting temporal layering. The Israeli case reveals additional initial evidence for an urgency effect, where news exposure boosts issue salience of some issues primarily on the immediate agenda. Overall, the study contributes to a deeper understanding of public agendas and news media effects, introducing a temporally nuanced perspective that enriches classical approaches to agenda-setting research.

The way digital news platforms represent migration issues can signifi-
cantly impact intergroup relations and policymaking. A recurring question in the
debate on the role of news platforms is whether they merely transmit informa-
tion on migration, or actively hype specific issues. Drawing on a comprehensive
set of socioeconomic statistics on migrants in Denmark, and employing a longitu-
dinal automated content analysis of migration news content, we utilize time-series
analysis to understand how four distinct categories of threat (security, economic,
cultural, and generalized) relate to socioeconomic data on terror attacks, migrant
crime levels, economic performance, and demographic trends. The results reveal a
direct effect of terror attacks, economic performance, and demographic trends on
migration news. We discuss the implications of socioeconomic and demographic
developments as factors in digital media content to understand the role of media
and substantiate contemporary debates

Central and Eastern Europe (CEE) countries faced significant political, economic, social, and technological transformations over the last four decades. Democratic processes, after relative stabilization, tremble again around polarizing values, populist leaders, or nationalistic ideologies. Online communication, especially social media platforms, play a vital role in shaping how citizens interact with the state, political actors, media, and other citizens. The book focuses on some of the challenges democratic institutions in the region face, in transforming and sustaining civil society and attempts to capture how the digital media environments mitigate or exacerbate those challenges. Included manuscripts focus on the role that online platforms play in the satisfaction with democracy in the CEE region, the interactions between journalists and political actors, the strategic media coverage of elections, affective polarization and political antagonism, and discursive attempts to discourage young people from civic engagement.

Journalists and experts play a pivotal role in communicating risks and helping the public navigate uncertain futures. This study examines the co-construction of projections by journalists and experts across news and social media during the Covid-19 pandemic. Unlike traditional news production, where journalists exercise agency by transforming expert knowledge into news narratives, hybrid media environments involve multi-platform, multi-directional, and non-linear processes of knowledge production. In light of these characteristics, we introduce and develop the concept of “predictive agency,” referring to an actor’s active participation in predictive knowledge-making and encompassing journalistic, civic, and epistemic forms of agency in shaping and navigating future-oriented knowledge. We analyse the trajectories of 400 projections in Israel and the US, tracing the interactional and informational dynamics between journalists and experts. Through qualitative textual analysis of the various iterations of each projection, four types of co-constructed projection systems emerge: Amplify, Distill, Elaborate, and Contest. We explore the complexities of predictive agency and accountability in these systems, shedding light on how collective futures are contested and co-constructed in hybrid media environments.

Communication research has long explored the association between media trust and news consumption. However, the strength and direction of this relationship have remained elusive. This study suggests a new approach for investigating these complex relations, differentiating between usage and trust associated with different sources over time. Focusing on the 2022 French election and drawing on data from a four-wave panel survey (N = 1,294), we utilized Random Intercept Cross-Lagged Panel Model (RI-CLPM) analysis to uncover two key over time effects: a selection effect, wherein trust reinforces usage; and a media effect, wherein usage influences trust. While a selection effect driven by news trust was observed in a right-wing political alternative channel, a media effect leading to news trust was linked with more traditional television channels. By identifying these effects and their associations with various types of outlets, this study advances the ongoing scholarly debate around the role of trust in news consumption.

This study investigates how elected representatives make promises to the electorate as part of their routine, everyday parliamentary discourse. Departing from the dominant focus on pre-election pledges in existing scholarship, we examine how representatives’ practices of making promises vary systematically over time (before/after elections/mid-term), by government role (government/opposition), party membership (catch-all/identity-based parties), and addressed electorate (inclusive/exclusive). Drawing upon research in speech acts, political discourse, and corpus pragmatics, we employ manual and computational text analysis on session transcripts from the Israeli parliament to identify variations in representatives’ use of direct or indirect action promises (N = 709), expressing different orientations of commitment. Documenting that promises continue to be prevalent after elections (especially among coalition members), we argue that the practices of making promises fulfill a critical role in representation that extends far beyond the election campaign, maintaining continuous accountability throughout the electoral cycle.

How do religious citizens’ election projections influence voter turnout? While previous studies have demonstrated the significant impact of religious orientation on individuals’ general future outlook, little is known about the influence of religion on voters’ electoral expectations and how these expectations affect voter turnout. In this paper, we employ a nuanced conceptual framework of election projections and examine the impact of religion on both the affective and probabilistic aspects of citizens’ expectations regarding election outcomes. Our analysis draws upon original panel survey data collected in two countries, focusing on the 2021 Israeli general elections and the 2022 French presidential elections. The findings reveal a mobilizing effect of religious citizens’ election projections in both Israel and France. Specifically, religious voters tend to have more positive affective forecasts about their projected election outcomes, consequently resulting in increased voter turnout. While affective forecasting plays a significant role in religious citizens’ turnout, probabilistic certitude does not have a similar effect. We discuss the contribution and implications of these findings for research on religion and political behavior.

We introduce and implement a novel dimension-reduction method for high-dimensional time-varying contingency-tables: the Evolutionary Correspondence Analysis (ECA). ECA enables a comparative analysis of high-dimensional, diachronic processes by identifying a small number of shared latent variables that shape co-evolving data patterns. ECA offers new opportunities for the study of complex social phenomena, such as co-evolving public debates: Its capacity to inductively extract time-varying latent variables from observed contents of evolving debates permits an analysis of meanings shared by linked sub-discourses, such as linked national public spheres or the discourses led by distinct political camps within a shared public sphere. We illustrate the utility of our approach by studying how the Greek and German right-, centre-, and left-leaning news coverage of the European financial crisis evolved between its outbreak in 2009 until its institutional containment in 2012. Comparing the use of 525 unique concepts in six German and Greek outlets with different political leaning over an extended period of time, we identify two common factors accounting for those evolving meanings and analyse how the different sub-discourses influenced one another over time. We allow the factor loadings to be time-varying, and fit to the latent factors a time-varying vector-auto-regressive model with time-varying mean.

CEE countries faced significant political, economic, social, and technological transformations over the last four decades. Democratic processes, after relative stabilization, tremble again around polarizing values, populist leaders, or nationalistic ideologies. Online communication, especially social media platforms, play a vital role in shaping how citizens interact with the state, political actors, media, and other citizens. The collection of manuscripts focuses on some of the challenges democratic institutions in the region face, in transforming and sustaining civil society and attempts to capture how the digital media environments mitigate or exacerbate those challenges. Included manuscripts focus on the role that online platforms play in the satisfaction with democracy in the CEE region, the interactions between journalists and political actors, the strategic media coverage of elections, affective polarization and political antagonism, and discursive attempts to discourage young people from civic engagement.

In this paper, we propose a new strategy for classifying evaluations in large text corpora, using supervised machine learning (SML). Departing from a conceptual and methodological critique of the use of sentiment measures to recognize object-specific evaluations, we argue that a key challenge consists in determining whether a semantic relationship exists between evaluative expressions and evaluated objects. Regarding sentiment terms as merely potentially evaluative expressions, we thus use a SML classifier to decide whether recognized terms have an evaluative function in relation to the evaluated object. We train and test our classifier on a corpus of 10,004 segments of election coverage from 16 major U.S. news outlets and Tweets by 10 prominent U.S. politicians and journalists. Specifically, we focus on evaluations of political predictions about the outcomes and implications of the 2016 and 2020 U.S. presidential elections. We show that our classifier consistently outperforms both off-the-shelf sentiment tools and a pre-trained transformer-based sentiment classifier. Critically, our classifier correctly discards numerous non-evaluative uses of common sentiment terms, whose inclusion in conventional analyses generates large amounts of false positives. We discuss contributions of our approach to the measurement of object-specific evaluations and highlight challenges for future research.

This paper explores trust-building strategies in future-oriented news discourse, marked by a high degree of uncertainty. While current research mainly focuses on audiences’ perceptions of news credibility, this study addresses news trust from a production standpoint. We examine the trust-building efforts of media actors, focusing on their discursive labor within the context of election projections. Drawing on rich data from five election rounds in Israel and the US, we qualitatively analyzed 400 news texts and 400 tweets that were produced by 20 US and 20 Israeli media actors. This textual analysis was supplemented by 10 in-depth interviews with Israeli journalists. Our findings demonstrate three types of journalistic trust-building rhetoric in election coverage: facticity, authority, and transparency. These strategies result in a two-fold form of trust, which re-affirms traditional notions of accuracy and validity, while also challenging the ability of newspersons to obtain them in contemporary political and media cultures. Overall, these strategies hold unique opportunities and challenges for sustaining public trust in journalism and illuminate the complex communicative labor involved in building trust with news audiences. Our findings also highlight the importance of studying trust not only in relation to the past and the present, but also in future-oriented discourse.

In quantitative content analysis, conventional wisdom holds that reliability, operationalized as agreement, is a necessary precondition for validity. Underlying this view is the assumption that there is a definite, unique way to correctly classify any instance of a measured variable. In this intervention, we argue that there are textual ambiguities that cause disagreement in classification that is not measurement error, but reflects true properties of the classified text. We introduce a notion of valid disagreement, a form of replicable disagreement that must be distinguished from replication failures that threaten reliability. We distinguish three key forms of meaning multiplicity that result in valid disagreement – ambiguity due to under-specification, polysemy due to excessive information, and interchangeability of classification choices – that are widespread in textual analysis, yet defy treatment within the confines of the existing content-analytic toolbox. Discussing implications, we present strategies for addressing valid disagreement in content analysis.

As long as we have attempted to sanction untoward speech, others have devised strategies for expressing themselves while dodging such sanctions. In this intervention, I review the arms race between technological filters designed to curb hate speech, and evasive language practices designed to avoid detection by these filters. I argue that, following important advances in the detection of relatively overt uses of hate speech, further advances will need to address hate speech that relies on culturally or situationally available context knowledge and linguistic ambiguities to convey its intended offenses. Resolving such forms of hate speech not only poses increasingly unreasonable demands on available data and technologies, but does so for limited, uncertain gains, as many evasive uses of language effectively defy unique valid classification.